Using brain signals patterns for biometric identity verification systems

Abstract

In the natural human computer interaction filed, researchers started to consider the other interaction modalities for diversity of applications. Among these modalities are the speech interaction systems, eye gaze interaction systems and recently Brain Computer Interfacing (BCI) systems. In BCI systems, the tools are deployed to manipulate the brain activity to produce signals that can be used to control computers or communication devices. Implementing this technology in real life varies from: entertainment systems to control layers through the user thoughts, to disability assistive devices to reduce care given. Currently the BCI technologies are developed for the purposes of boosting the disability assistive devices especially in the command controlled systems. In addition, the currently demanding research emphasis is to use brain signals for personal identifications and verification; known as biometric verification. Biometric verification was first used in as an authentication technique for systems operating devices in real environment. At that time, authentication was based on unimodal biometric identity verification systems, which compare only one trait or biometrical feature (such as voice, iris, or fingerprint) to a previous sample. However, the performance of such modals varies depending on the presence of outside factors such as background noises in a speech recognition system, or the illumination problems for a face recognition system. Another cause of pitfalls in these models is their dependency on the health of the authenticated user. In order to overcome the weaknesses of the unimodal biometric system, a multimodal biometric system was introduced.